The integration of sequential movements: The interaction between switching movement direction and switching hand at the first target
Bibliographic record
Abstract
Movement times to a single target are typically shorter compared to when a second target is required (i.e., the one-target advantage). The phenomenon emerges regardless of hand preference, the amount of practice or visual feedback available, and when participants switch hands at the first target. Our goal was to investigate the interactive effects between switching hands and changing movement direction at the first target on the one-target advantage. Participants performed movements to a single target; movements to two targets with a single hand where the second target required an extension movement; movements to two targets with a single hand where the second target required a reversal movement; movements to two targets where the hands were switched at the first target and the second target required an extension movement; and movements to two targets where the limbs were switched at the first target and the second movement required a reversal. RTs were significantly faster in the single target compared to the two target tasks. Furthermore, MTs to the first target were significantly shorter in the single target compared to two target one hand extension, two target two hand extension, and the two target two hand reversal tasks. The finding that the one-target advantage emerged when the hands were switched at the first target suggests the phenomenon occurs at the central level and can thus be explained by the movement integration hypothesis. Elimination of the one-target advantage in the two target single hand reversal task suggests that the two target advantage occurs within a limb at the peripheral level. This is likely due to the deceleration and acceleration of the first and second movement utilising the same muscles, whereas these processes are controlled by two separate and distinct effectors when the hands are switched at the first target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".